New York Startup Index

New York Startup Index provides an interactive view of the good and bad of the city, presented in a way that allows you to easily determine the best and worst places to launch a startup. Information about public transportation, local businesses, and crime information are all aggregated on a single map to give you a quick overlay of both historical and real-time information that will allow you to quickly decide where you want to launch a startup.

What's cool about it

Have you ever wondered what the most dangerous parts of New York City were? Or maybe where other tech companies are in the city? The interactive maps provided by New York Startup Index provide all of the information, and more. You can look around the map and unlock the hidden data you never knew existed as you are presented with information on the most common crimes (and the locations that they were reported), places to pick up public transportation, and where other tech companies are around you.

As not all information is relevant to everyone, we provide awesome filters that will let you display only the information you care about. And by using highly scalable technologies like MongoDB and open data sets that are freely available, the open source code base makes it possible to implement this for other cities and fields.

What inspired us

After watching numerous talks at conferences where people worked with open data sets to solve important problems, like finding the two fire hydrants that raked in the most cash in parking violations, we were inspired to use open data in this hack. After seeing the interesting data sets that were made available with New York City's push for releasing open data sets, we decided to pull together some of the most informative ones to plot together on a common map.

How we did it

New York Startup Index takes multiple open data sets and provides an interactive map that allows you to move around the city and see how different areas compare. While some of the data was available in the end format that we were looking for, GeoJSON, we needed to write some custom scripts to parse CSV files and extract the important information from them so we could convert them to a usable GeoJSON format. This data was then stored in a MongoDB database, allowing us to use their excellent geospatial data support to filter down the data sets to just the locations we were looking for.

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I worked on retrieving and normalizing the data from the open data sets. As different open data sets use different schemas and data formats (kml, csv, etc.) normalizing the data to have a consistent, queryable format was an important part of the process.

On the backend I also assisted in getting the geospatial data working with MongoDB so we could retrieve only relevant points on the map. We normalized all of the data to use GeoJSON, giving us a relatively consistent format that could be stored by MongoDB will some small changes. MongoDB handles the geospatial data fairly well, which made the querying part of the backend relatively easy to implement so we could convert the data back to the GeoJSON format that we needed.

Apparently everyone on the team is very verbose, but they were great to work with.

I set up mongodb with a restful api for querying geojson entries. This was my first time working with geospatial indexing so it was really interesting to see how mongodb handles these lookups using geometric parameters.

I also tested out a few mapping api services such as google maps, openstreetmap and Leaflet. Leaflet's javascript really provides the most comprehensive way to interact with maps so we chose that one for our project. I set up a mapbox account to use with Leaflet and created a map in which coordinates of various locations in our database were displayed as translucent circles to create a heatmap effect. Leaflet also supports map pins so I set those up to display the names for each tech company.

Altogether this was one of the best hackathons that I have been to so far. Working on this project was really enjoyable and I'm extremely proud of what our team came up with.

I worked on creating the front-end for the project, helped with gathering and converting data, and focused heavily on the planning of the project.

The front-end is a mix of jQuery and CSS for the navigation. A chunk of the data we had found that we wanted to use was formatted as large CSVs, so I worked to split it up into GeoJSON information for our MongoDB database. Throughout the project, I worked to make sure we kept the idea of what we were working on clear and helped design the project.

This was my first hackathon ever! I'm admittedly inexperienced but I had an amazing time working on this project with Gunther, Kevin, and Zane. I'm proud of what we did and I'm excited to come back to HackNY next time they host a hackathon!

I worked on finding the databases we needed, and focused on the index calculations for the varying demographics -- along with assisting in front-end design. I was also the presenter of the project.

This was my first hackathon as well, and it was a day of firsts. The first time ever going on a train, or a subway, or a hackithon, or using a no-SQL database. I have to say, it was one of the most eye-opening experiences I've ever had. Going in, I was intimidated by all of the new elements that we had to use -- but by the end I realized that this is probably the best way to see what's out there and learn something new.

I'm definitely looking forward to coming back next year, and working with this team was an outstanding experience.